Argus：智能手机支持的人类合作，通过多智能体强化学习实现灾害态势感知
Argus: Smartphone-enabled Human Cooperation via Multi-Agent Reinforcement Learning for Disaster Situational Awareness
摘要：
Argus利用多智能体强化学习（MARL）框架，使用事故区域周围的智能体创建灾难现场的3D地图，以促进救援行动。这些智能体既可以是灾难现场的人类旁观者，也可以是可以帮助人类的无人机或机器人。代理参与使用智能手机（或无人机情况下的机载摄像头）捕获场景的图像，由MARL算法指导。这些图像被用来构建灾难现场的实时的3D地图。通过模拟和真实的实验，评估的框架在跟踪环境的随机动态性方面的有效性。
英文摘要：
Argus exploits a Multi-Agent Reinforcement Learning (MARL) framework to create a 3D mapping of the disaster scene using agents present around the incident zone to facilitate the rescue operations. The agents can be both human bystanders at the disaster scene as well as drones or robots that can assist the humans. The agents are involved in capturing the images of the scene using their smartphones (or on-board cameras in case of drones) as directed by the MARL algorithm. These images are used to build real time a 3D map of the disaster scene. Via both simulations and real experiments, an evaluation of the framework in terms of effectiveness in tracking random dynamicity of the environment is presented.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
